README.md

float8_experimental

This is an early version of a library for accelerating training with float8 in native PyTorch according to the recipes laid out in https://arxiv.org/pdf/2209.05433.pdf. The codebase strives to stay small, easily hackable, and debuggable with native PyTorch tooling. torch.compile is supported out of the box. With torch.compile on, initial results show throughput speedups of up to 1.2x on small scale (8 GPUs) LLaMa pretraining jobs.

⚠️ See the feature tracker for upcoming features. Key features such as weight cast recomputation in backward and large scale distributed support are not ready yet.

⚠️ Backwards compatibility is not guaranteed at this point. The codebase is in active development and will change rapidly.

installation

⚠️ For now, use the latest PyTorch nightly for best results with torch.compile.

pip install .

# Optionally install editable
pip install -e .

# Optionally Install dev tooling
pip install -e ".[dev]"

User API

We provide two per-tensor scaling strategies: dynamic and delayed. See https://arxiv.org/pdf/2209.05433.pdf, Section 4.3 for more details. These strategies are configurable separately for activations (x), weights (w) and gradients (dL_dY).

float8 linear with dynamic scaling for x, w and dL_dY

This is the most accurate recipe as every tensor is scaled dynamically.

from float8_experimental.float8_linear_utils import (
    swap_linear_with_float8_linear,
)
from float8_experimental.float8_linear import Float8Linear

# create model
m = Model(...)

# convert all `torch.nn.Linear` modules to `Float8Linear`
swap_linear_with_float8_linear(m, Float8Linear)

# optional: use FSDP
model = FSDP(model, use_orig_params=True)

# optional: enable torch.compile for improved performance
m = torch.compile(m)

# train/finetune (not shown)

float8 linear with delayed scaling

This is theoretically the most performant recipe as it minimizes memory reads.

from float8_experimental.float8_linear_utils import (
    swap_linear_with_float8_linear,
    sync_float8_amax_and_scale_history,
)
from float8_experimental.float8_linear import Float8Linear, TensorScalingType

# create model
m = Model(...)

# convert all `torch.nn.Linear` modules to `Float8Linear`, specifying scaling
# type
swap_linear_with_float8_linear(
    m, 
    Float8Linear,
    scaling_type_x=TensorScalingType.DELAYED,
    scaling_type_w=TensorScalingType.DELAYED,
    scaling_type_dL_dY=TensorScalingType.DELAYED,
)

# optional: use FSDP. Note that workarounds gated with config.enable_amax_init and
# config.enable_pre_and_post_forward are needed for autocast + compile + FSDP + float8 to work
from float8_experimental import config
config.enable_amax_init = False  # only needed for autocast + compile + FSDP +  float8 delayed
config.enable_pre_and_post_forward = False  # only needed for autocast + compile + FSDP +  float8 delayed
model = FSDP(model, use_orig_params=True)

# optional: enable torch.compile for improved performance
m = torch.compile(m)

# toy training loop
for _ in range(N_ITER):
    optimizer.zero_grad()
    y = m(x)
    y.sum().backward()

    # specific to float8 with delayed scaling: separate step to sync scales/amaxes
    # in the future, this may move to a context manager
    sync_float8_amax_and_scale_history(model)

    optimizer.step()

🧭 Code Organization

• float8_experimental/float8_linear.py
  • Float8Linear (main user facing entry point for Float8Linear)
• float8_experimental/float8_tensor.py
  • Float8Tensor, which allows Float8Linear to abide by the x.dtype == x.grad.dtype restriction
  • ScaledMMConfig defines the semantics for matmul in the forward and backwards pass

Testing

# run single-GPU unit tests
pytest test/test_base.py

# run a single-GPU integration test on SAM
pytest test/test_sam.py

# run single-GPU compile tests
pytest test/test_compile.py
# run a two-GPU integration test on FSDP
./test/test_fsdp.sh

# run integration tests for TP/SP (outdated)
./test/test_tp.sh

# run all of these tests
./test/test_everything.sh

Benchmarking

# benchmark the torch._scaled_mm function on LLaMa 2 70B shapes
./benchmarks/bench_matmul.py

# benchmark fw/bw of `Linear` and `Float8Linear` on LLaMa 2 70B shapes
# make sure to turn on torch.compile to get the best performance
./benchmarks/bench_linear_float8.py -o ../tmp/test.txt --compile

License

PyTorch has a BSD 3-Clause License, as found in the LICENSE file.